MO3.T10.5Ahmed.ppt
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The document describes a new algorithm for correcting the bidirectional reflectance distribution function (BRDF) of remote sensing reflectance measurements in coastal waters. Through radiative transfer simulations for a wide range of conditions, the authors developed a model relating remote sensing reflectance to the inherent optical properties of coastal waters. The new BRDF correction algorithm was shown to reduce uncertainty to below 1% when applied to both in situ and satellite ocean color data, improving on an existing open ocean algorithm. Its application significantly improved the match between in situ and MODIS satellite measurements of coastal waters.
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For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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MO3.T10.5Ahmed.ppt
- 1. Bidirectional Reflectance Function in Coastal Waters And its Application to the Validation of the Ocean Color Satellites Alexander Gilerson 1 , Soe Hlaing 1 , Tristan Harmel 1 , Alberto Tonizzo 1 , Robert Arnone 2 , Alan Weidemann 2 , Samir Ahmed 1 1 Optical Remote Sensing Laboratory, City College, New York 2 Naval Research Laboratory, Stennis Space Center
- 6. Theoretical Background Fundamental equation which relates Rrs to optical properties [Morel 2002 et. al] : merges reflection and refraction effects that occur when downward irradiance and upward radiance propagate through the air-water interface f relates the magnitude of the irradiance reflectance just below the surface to IOP Angular Coordinate Convention θ v ~ Viewing angle θ s ~ solar Zenith φ ~ solar-sensor relative azimuth BRDF correction: Set f and Q for Sun at zenith and nadir view Rrs ( W,IOP ) _corrected Q= bidirectional function W = wind speed ω = single back-scattering albedo ω = b b / ( a + b b ) determined by IOP
- 7. Bio-optical model and radiative transfer simulation 1053 sets of Viewing & illumination geometries Viewing angle ( θ v ) 0 o ~ 80 o solar Zenith ( θ s ) 0 o ~ 80 0 relative azimuth ( φ ) 0 o ~ 180 o Wavelength: 412,443, 491, 551, 668 nm Inherent Optical Properties (IOP) Range of input parameters [Chl] = 1 to 10mg/m 3 C NAP = 0.01 to 2.5mg/m 3 a CDOM = 0 to 2m -1 ω = b b / ( a + b b ) can be directly connected to Rrs through modeling 500 sets of IOP Obtain Rrs ( λ ) & equivalent ω ( λ ) from 500 sets of IOPs to investigate Rrs – ω relatioships for large sets of viewing and illumination geometries. Generated as random variables in the prescribe ranges typical for coastal water conditions Particle Scattering Phase Function Varied with particle Concentration & Composition Radiative transfer simulations (Hydrolight) Remote-sensing Reflectance Rrs ( λ )
- 12. ASSESSMENT OF BRDF-CORRECTION APPLICATION TO ABOVE-WATER DATA AT LONG ISLAND SOUND COASTAL OBSERVATORY
- 14. Water type: Moderately turbid and very productive (Aurin et al. 2010) Bathymetry : plateau at 13 m depth Location and Bathymetry LISCO Site Characteristics Depth in meters (GEBCO data)
- 15. LISCO Tower LISCO site Characteristics Platform : Collocated multispectral SeaPRISM and hyperspectral HyperSAS instrumentations since October 2009 12 meters Retractable Instrument Tower Instrument Panel
- 18. Above Water Signal decomposition Above-Water Data Processing Sun T otal radiance Sky radiance Water leaving radiance Sea surface reflectivity Sun glint radiance E d Rrs = L w / E d Down-welling Irradiance Remote-sensing reflectance: Needs to be corrected for the bidirectionality property L i L w θ θ L T = L w + ρ (W) L i + L g L i
- 21. APPLICATION TO OCEAN COLOR MODIS IMAGERY
- 22. Satellite Validation Satellite Pixel Selection for Matchup Comparison 3km×3km pixel box for matchup comparison Exclusion of pixel box if presence of cloud-contaminated pixels in this 9km×9km pixel box Validation of MODIS-Aqua against the LISCO Data Satellite Data Processing: Standard NASA Ocean Color Reprocessing 2009 Also exclusion of any pixel flagged by the NASA data quality check processing (Atmospheric correction failure, sun glint contamination,…)
- 23. Rrs Time series for the match-up comparison Comparison between LISCO and MODIS Ocean Color data Qualitative consistency in variations is observed between the in-situ and satellite data. How will the Satellite / in situ data comparison be improved by application of the CCNY BRDF-correction ?
- 26. ACKNOWLEDGMENTS NASA AERONET team for SeaPRISM calibration, data processing and support of the site operations NASA Ocean Color Processing Group for satellite imagery Partial support from: Office of Naval Research (ONR) National Oceanographic and Atmospheric Administration (NOAA)